AWS News Blog
Author: Antje Barth
Antje Barth is a Principal Developer Advocate for generative AI at AWS. She is co-author of the O’Reilly books Generative AI on AWS and Data Science on AWS. Antje frequently speaks at AI/ML conferences, events, and meetups around the world. She also co-founded the Düsseldorf chapter of Women in Big Data.
New for Amazon SageMaker – Perform Shadow Tests to Compare Inference Performance Between ML Model Variants
As you move your machine learning (ML) workloads into production, you need to continuously monitor your deployed models and iterate when you observe a deviation in your model performance. When you build a new model, you typically start validating the model offline using historical inference request data. But this data sometimes fails to account for […]
Next Generation SageMaker Notebooks – Now with Built-in Data Preparation, Real-Time Collaboration, and Notebook Automation
In 2019, we introduced Amazon SageMaker Studio, the first fully integrated development environment (IDE) for data science and machine learning (ML). SageMaker Studio gives you access to fully managed Jupyter Notebooks that integrate with purpose-built tools to perform all ML steps, from preparing data to training and debugging models, tracking experiments, deploying and monitoring models, […]
New – Share ML Models and Notebooks More Easily Within Your Organization with Amazon SageMaker JumpStart
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. SageMaker JumpStart gives you access to built-in algorithms with pre-trained models from popular model hubs, pre-trained foundation models to help you perform tasks such as article summarization and image generation, and end-to-end solutions to solve common use cases. […]
New ML Governance Tools for Amazon SageMaker – Simplify Access Control and Enhance Transparency Over Your ML Projects
As companies increasingly adopt machine learning (ML) for their business applications, they are looking for ways to improve governance of their ML projects with simplified access control and enhanced visibility across the ML lifecycle. A common challenge in that effort is managing the right set of user permissions across different groups and ML activities. For […]
New – Redesigned UI for Amazon SageMaker Studio
Today, I’m excited to announce a new, redesigned user interface (UI) for Amazon SageMaker Studio. SageMaker Studio provides a single, web-based visual interface where you can perform all machine learning (ML) development steps with a comprehensive set of ML tools. For example, you can prepare data using SageMaker Data Wrangler, build ML models with fully […]
Amazon Connect – New ML-Powered Capabilities for Forecasting, Capacity Planning, Scheduling, and Agent Empowerment
Amazon Connect is an easy-to-use cloud contact center that helps companies of any size deliver superior customer service at a lower cost. If you are following our Amazon Connect announcements, you likely noticed that we keep adding more and more machine learning (ML) powered capabilities to Amazon Connect. ML makes Amazon Connect already smarter at […]
AWS Week in Review – October 31, 2022
No tricks, just treats in this weekly roundup of news and announcements. Let’s switch our AWS Management Console into dark mode and dive right into it. Last Week’s Launches Here are some launches that got my attention during the previous week: AWS Local Zones in Hamburg and Warsaw now generally available – AWS Local Zones help […]
Amazon EC2 Trn1 Instances for High-Performance Model Training are Now Available
Update April 13, 2023 — Amazon Elastic Compute Cloud (EC2) Trn1n instances, powered by AWS Trainium, are now generally available. Amazon EC2 Trn1n instances double the network bandwidth (compared to Trn1 instances) to 1600 Gbps of Elastic Fabric Adapter (EFA) to deliver even higher performance for training network-intensive generative artificial intelligence (AI) models, such as large […]